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Relevant bibliographies by topics / IMAGE PATCHING

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Journal articles

Academic literature on the topic 'IMAGE PATCHING'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "IMAGE PATCHING"

1

Tsai, Yi-Chang (James), Yi-Ching Wu, and Geoffrey Price. "A Cost-Effective and Objective Full-Depth Patching Identification Method using 3D Sensing Technology with Automated Crack Detection and Classification." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (2018): 50–58. http://dx.doi.org/10.1177/0361198118798474.

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Full-depth patching is one of the commonly used asphalt pavement maintenance and rehabilitation methods in which deteriorated base and surface layers are repaired to restore strength and improve ride quality. During resurfacing projects, areas requiring full-depth patching are identified and quantified as construction priorities because of the high costs associated with the labor and materials for the procedure. Currently, the manual surveys conducted to identify these locations are time-consuming and labor-intensive. Thus, large projects often cannot easily quantify the full-depth patching ne

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2

Zhong, Haidong, Xianyi Chen, and Qinglong Tian. "An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching." Information 10, no. 1 (2019): 17. http://dx.doi.org/10.3390/info10010017.

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Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method f

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3

Gong, Yan. "Panoramic Image Patching Algorithm Based on Global Optimization." Journal of Information and Computational Science 12, no. 14 (2015): 5523–30. http://dx.doi.org/10.12733/jics20150019.

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4

Celaya-Padilla, Jose M., Carlos E. Galvan T, J. Ruben Delgado C, Issac Galvan-Tejada, and Ernesto Ivan Sandoval. "Multi-seed texture synthesis to fast image patching." Procedia Engineering 35 (2012): 210–16. http://dx.doi.org/10.1016/j.proeng.2012.04.182.

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5

Totsuka, Satoru, Tomoya Handa, Hitoshi Ishikawa, and Nobuyuki Shoji. "Improvement of Adherence with Occlu-Pad Therapy for Pediatric Patients with Amblyopia." BioMed Research International 2018 (November 22, 2018): 1–5. http://dx.doi.org/10.1155/2018/2394562.

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We aimed to examine visual acuity improvement effect and adherence in amblyopia training using tablet type vision training equipment (Occlu-pad). The subjects were 138 patients with amblyopia (average age of 5.5 ± 1.6 years old); their amblyopic visual acuity at the start of training was logMAR 0.15 to 1.3. Occlu-pad is a device that processes images such that amblyopic eyes can only view the image as it passes through polarized glasses; this is achieved by peeling off the polarizing film layer in the liquid crystal display of an iPad (Apple). Amblyopia training comprised either the instructio

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6

Dan, Han-Cheng, Hao-Fan Zeng, Zhi-Heng Zhu, Ge-Wen Bai, and Wei Cao. "Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network." Sustainability 14, no. 2 (2022): 861. http://dx.doi.org/10.3390/su14020861.

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Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural net

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7

TOKUDA, Kenichi, Tetsuya KINUGASA, Ryota HAYASHI, Takafumi HAJI, and Hisanori AMANO. "Shredded Image Patching of Inner Crawler Cameras for Disaster Robot." Proceedings of Mechanical Engineering Congress, Japan 2016 (2016): G1500504. http://dx.doi.org/10.1299/jsmemecj.2016.g1500504.

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8

Takahashi, Ryo, Takashi Matsubara, and Kuniaki Uehara. "Data Augmentation Using Random Image Cropping and Patching for Deep CNNs." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (2020): 2917–31. http://dx.doi.org/10.1109/tcsvt.2019.2935128.

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9

Maeda, Keisuke, Saya Takada, Tomoki Haruyama, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching." Sensors 22, no. 22 (2022): 8932. http://dx.doi.org/10.3390/s22228932.

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Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress det

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10

WANG, DACHENG, and SARGUR N. SRIHARI. "ANALYSIS OF FORM IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (1994): 1031–52. http://dx.doi.org/10.1142/s0218001494000528.

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Automatic analysis of images of forms is a problem of both practical and theoretical interest; due to its importance in office automation, and due to the conceptual challenges posed for document image analysis, respectively. We describe an approach to the extraction of text, both typed and handwritten, from scanned and digitized images of filled-out forms. In decomposing a filled-out form into three basic components of boxes, line segments and the remainder (handwritten and typed characters, words, and logos), the method does not use a priori knowledge of form structure. The input binary image

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